class_docstr: EdgeConv layer from `Dynamic Graph CNN for Learning on Point Clouds
  <https://arxiv.org/pdf/1801.07829>`__
  
  It can be described as follows:
  
  .. math::
  
     h_i^{(l+1)} = \max_{j \in \mathcal{N}(i)} (
     \Theta \cdot (h_j^{(l)} - h_i^{(l)}) + \Phi \cdot h_i^{(l)})
  
  where :math:`\mathcal{N}(i)` is the neighbor of :math:`i`,
  :math:`\Theta` and :math:`\Phi` are linear layers.
  
  .. note::
  
     The original formulation includes a ReLU inside the maximum operator.
     This is equivalent to first applying a maximum operator then applying
     the ReLU.
  
  Parameters
  ----------
  in_feat : int
      Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`.
  out_feat : int
      Output feature size; i.e., the number of dimensions of :math:`h_i^{(l+1)}`.
  batch_norm : bool
      Whether to include batch normalization on messages. Default: ``False``.
  allow_zero_in_degree : bool, optional
      If there are 0-in-degree nodes in the graph, output for those nodes will be invalid
      since no message will be passed to those nodes. This is harmful for some applications
      causing silent performance regression. This module will raise a DGLError if it detects
      0-in-degree nodes in input graph. By setting ``True``, it will suppress the check
      and let the users handle it by themselves. Default: ``False``.
  
  Note
  ----
  
  Zero in-degree nodes will lead to invalid output value. This is because no message
  will be passed to those nodes, the aggregation function will be appied on empty input.
  A common practice to avoid this is to add a self-loop for each node in the graph if
  it is homogeneous, which can be achieved by:
  
  >>> g = ... # a DGLGraph
  >>> g = dgl.add_self_loop(g)
  
  Calling ``add_self_loop`` will not work for some graphs, for example, heterogeneous graph
  since the edge type can not be decided for self_loop edges. Set ``allow_zero_in_degree``
  to ``True`` for those cases to unblock the code and handle zero-in-degree nodes manually.
  A common practise to handle this is to filter out the nodes with zero-in-degree when use
  after conv.
docstr: Set allow_zero_in_degree flag.
  
  Parameters
  ----------
  set_value : bool
      The value to be set to the flag.
docstr_tok: ["Set","allow_zero_in_degree","flag",".","Parameters","--","--","--","--","--","set_value",":","bool","The","value","to","be","set","to","the","flag","."]
code: def set_allow_zero_in_degree(self, set_value):
      r"""Set allow_zero_in_degree flag.
  
      Parameters
      ----------
      set_value : bool
          The value to be set to the flag.
      """
      self._allow_zero_in_degree = set_value
  
file_path: dgl/python/dgl/nn/tensorflow/conv/edgeconv.py
imports: import tensorflow
  from tensorflow.keras import layers
  from ....None import function
  from ....base import DGLError
  from ....utils import expand_as_pair
label_desc: Use image node_id 2 for calling the EdgeConv obj's underlying member method code with example usage: obj.set_allow_zero_in_degree(set_value) without return types